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BMC Bioinformatics DOI:10.1186/s12859-019-2750-4

A (fire)cloud-based DNA methylation data preprocessing and quality control platform.

Publication TypeJournal Article
Year of Publication2019
AuthorsKangeyan, D, Dunford, A, Iyer, S, Stewart, C, Hanna, M, Getz, G, Aryee, MJ
JournalBMC Bioinformatics
Volume20
Issue1
Pages160
Date Published2019 Mar 29
ISSN1471-2105
KeywordsCloud Computing, Databases, Nucleic Acid, DNA Methylation, Genome, Human, Genomics, Humans, Quality Control, Reproducibility of Results, Sequence Analysis, DNA, Software, Whole Genome Sequencing, Workflow
Abstract

BACKGROUND: Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets.

RESULTS: We present a set of preprocessing pipelines for bisulfite sequencing DNA methylation data that include a new R/Bioconductor package, scmeth, for a series of efficient QC analyses of large datasets. The pipelines go from raw data to CpG-level methylation estimates and can be run, with identical results, either on a single computer, in an HPC cluster or on Google Cloud Compute resources. These pipelines are designed to allow users to 1) ensure reproducibility of analyses, 2) achieve scalability to large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with thousands of cells, 3) enable integration and comparison between user-provided data and publicly available data, as all samples can be processed through the same pipeline, and 4) access to best-practice analysis pipelines. Pipelines are provided for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and hybrid selection (capture) bisulfite sequencing (HSBS).

CONCLUSIONS: The workflows produce data quality metrics, visualization tracks, and aggregated output for further downstream analysis. Optional use of cloud computing resources facilitates analysis of large datasets, and integration with existing methylome profiles. The workflow design principles are applicable to other genomic data types.

DOI10.1186/s12859-019-2750-4
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/30922215?dopt=Abstract

Alternate JournalBMC Bioinformatics
PubMed ID30922215
PubMed Central IDPMC6440105
Grant ListT32 CA009337 / CA / NCI NIH HHS / United States
Broad NCI Cloud Pilot Project / / National Cancer Institute /
T32 CA 009337-37 / / National Cancer Institute /
SPARC Grant / / Broad Institute of MIT & Harvard /
Merkin Institute Fellowship / / Broad Institute of MIT & Harvard /